Papers › HybridPose: 6D Object Pose Estimation under Hybrid Representations
HybridPose: 6D Object Pose Estimation under Hybrid Representations
Chen Song, Jiaru Song, Qi-Xing Huang
We introduce HybridPose, a novel 6D object pose estimation approach. HybridPose utilizes a hybrid intermediate representation to express different geometric information in the input image, including keypoints, edge vectors, and symmetry correspondences. Compared to a unitary representation, our hybrid representation allows pose regression to exploit more and diverse features when one type of predicted representation is inaccurate (e.g., because of occlusion). Different intermediate representations used by HybridPose can all be predicted by the same simple neural network, and outliers in predicted intermediate representations are filtered by a robust regression module. Compared to state-of-the-art pose estimation approaches, HybridPose is comparable in running time and accuracy. For example, on Occlusion Linemod dataset, our method achieves a prediction speed of 30 fps with a mean ADD(-S) accuracy of 47.5%, representing a state-of-the-art performance. The implementation of HybridPose is available at https://github.com/chensong1995/HybridPose.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 6D Pose Estimation using RGB | LineMOD | HybridPose | Accuracy (ADD) | 94.5% | #10 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | LineMOD | HybridPose | Mean ADD | 91.3 | #10 of 22 | Archive leaderboard | report |
| 6D Pose Estimation using RGB | Occlusion LineMOD | HybridPose | Mean ADD | 47.5 | #7 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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